Abstract
With the rapid development of the global economy, people’s living standards have gradually improved, and the way of transportation has undergone earth-shaking changes. This has created a series of social and environmental issues while greatly facilitating people’s lives and work. Excessive numbers of cars have caused urban traffic problems such as traffic pollution, traffic jams and traffic accidents. The strong economic strength has made the automobile industry appear in a booming stage and made it enter the automobile society earlier. The license plate is a vehicle-passed ID card, so the detection and identification of the license plate has become one of the important research directions in today’s society. Although the resolution of today’s surveillance equipment is getting higher, there are still quite a few monitors with low resolution. Conventional license plate detection and recognition algorithms are challenged in low-resolution video processing. In this paper, we introduce the theory and methodology of GAN and transfer learning, which are applied to deal with the license plate image recognition under several complex environments. The experimental results show that the method adopted in this paper has higher recognition rate and robustness.
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This project is supported by the Fundamental Research Funds for the Central Universities, Grant Number 2017JBM066.
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Du, X. Complex environment image recognition algorithm based on GANs and transfer learning. Neural Comput & Applic 32, 16401–16412 (2020). https://doi.org/10.1007/s00521-019-04018-x
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DOI: https://doi.org/10.1007/s00521-019-04018-x